Papers Android Malware Detection
“Android Malware Detection” 태그가 달린 논문 76편 · 필터 해제
Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection
Malware authors have seen obfuscation as the mean to bypass malware detectors based on static analysis features. For Android, several studies have confirmed that many anti-malware products are easily evaded with simple p…
Android Malware DetectionMalware DetectionEfficient Concept Drift Handling for Batch Android Malware Detection Models
The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the ex…
Active LearningAndroid Malware DetectionDrift DetectionMalware DetectionEfficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting
The widespread adoption of the Android operating system has made malicious Android applications an appealing target for attackers. Machine learning-based (ML-based) Android malware detection (AMD) methods are crucial in …
Android Malware DetectionMalware DetectionLaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning
Transfomer-based models have significantly advanced natural language processing, in particular the performance in text classification tasks. Nevertheless, these models face challenges in processing large files, primarily…
Android Malware DetectionClassificationDefect DetectionDocument Classification+7On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities
As the smartphone market leader, Android has been a prominent target for malware attacks. The number of malicious applications (apps) identified for it has increased continually over the past decade, creating an immense …
Android Malware DetectionDimensionality ReductionMalware DetectionContinuous Learning for Android Malware Detection
Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malwa…
Active LearningAndroid Malware DetectionContrastive LearningMalware DetectionInvestigating Feature and Model Importance in Android Malware Detection: An Implemented Survey and Experimental Comparison of ML-Based Methods
The popularity of Android means it is a common target for malware. Over the years, various studies have found that machine learning models can effectively discriminate malware from benign applications. However, as the op…
Android Malware DetectionMalware DetectionMask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization
The vulnerability of smartphones to cyberattacks has been a severe concern to users arising from the integrity of installed applications (\textit{apps}). Although applications are to provide legitimate and diversified on…
Android Malware DetectionFederated LearningMalware DetectionTransfer LearningFlexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor
The behavior of malware threats is gradually increasing, heightened the need for malware detection. However, existing malware detection methods only target at the existing malicious samples, the detection of fresh malici…
Android Malware DetectionMalware DetectionLevel Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection
Machine Learning (ML) promises to enhance the efficacy of Android Malware Detection (AMD); however, ML models are vulnerable to realistic evasion attacks--crafting realizable Adversarial Examples (AEs) that satisfy Andro…
Adversarial RobustnessAndroid Malware DetectionMalware DetectionTransfer LearningTowards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning
As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feature sets have been…
Android Malware DetectionMalware DetectionA two-steps approach to improve the performance of Android malware detectors
The popularity of Android OS has made it an appealing target to malware developers. To evade detection, including by ML-based techniques, attackers invest in creating malware that closely resemble legitimate apps. In thi…
Android Malware DetectionBinary ClassificationClassificationContrastive Learning+2Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection
We propose the Malceiver, a hierarchical Perceiver model for Android malware detection that makes use of multi-modal features. The primary inputs are the opcode sequence and the requested permissions of a given Android A…
Android Malware DetectionMalware ClassificationMalware DetectionMaMaDroid2.0 -- The Holes of Control Flow Graphs
Android malware is a continuously expanding threat to billions of mobile users around the globe. Detection systems are updated constantly to address these threats. However, a backlash takes the form of evasion attacks, i…
Android Malware DetectionMalware DetectionAndroid Malware Detection using Feature Ranking of Permissions
We investigate the use of Android permissions as the vehicle to allow for quick and effective differentiation between benign and malware apps. To this end, we extract all Android permissions, eliminating those that have …
Android Malware DetectionMalware DetectionGraph Neural Network-based Android Malware Classification with Jumping Knowledge
This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set of program functions and their inter-proc…
Android Malware DetectionGraph Neural NetworkMalware ClassificationMalware Detection"How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector
AI methods have been proven to yield impressive performance on Android malware detection. However, most AI-based methods make predictions of suspicious samples in a black-box manner without transparency on models' infere…
Android Malware DetectionMalware DetectionEvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
Over the last decade, researchers have extensively explored the vulnerabilities of Android malware detectors to adversarial examples through the development of evasion attacks; however, the practicality of these attacks …
Adversarial AttackAndroid Malware DetectionMalware DetectionMORPHCan We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?
The deep learning approach to detecting malicious software (malware) is promising but has yet to tackle the problem of dataset shift, namely that the joint distribution of examples and their labels associated with the te…
Android Malware DetectionMalware DetectionDexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attractive to other fields such as malware detec…
Android Malware DetectionMalware DetectionRepresentation Learning